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Multi-Agent Task Allocation with Interagent Distance Constraints

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第2期21卷 168-177页

作者： Choi, Euihyeon Chang, Woohyuk Korea Adv Inst Sci & Technol Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea Agcy Def Dev Aerosp Technol Res Inst Yuseong POB 35 Daejeon 34186 South Korea

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

关键词： mixed integer linear programming Autonomous Technology Robots Task Allocation Path Planning Traveling Salesman Problem Inter-Agent Distance Constraints Multi-Agent System

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A matheuristic-oriented iterated greedy algorithm for multi-mode resource-constrained project scheduling problem under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Gao, Zheng Zhang, Liping Yu, Puliang Zhang, Zikai Li, Zixiang Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China

Multi-mode resource-constrained project scheduling problem is one of the most important branches of combinatorial optimization problems. The aim is to find a feasible solution consisting of activity start time and execution mode so that the project makespan is minimized. However, there are a lot of uncertain events, that bring great challenges to project scheduling. We first propose a mixed integer linear programming model where the activity duration is uncertain. Then two uncertain parameters are introduced to describe the disturbance degree of uncertain activity duration and the allowable violation degree of constraints respectively, and the MILP model with uncertain activity duration is converted to its deterministic robust counterpart model. A matheuristic local optimization approach is proposed to balance computational time with the optimization of the computational result. A matheuristic-oriented iterated greedy algorithm that combines a matheuristic local optimization approach and an iterated greedy algorithm is proposed to solve this problem in this paper. Experimental results indicate that the robust counterpart model can obtain robust optimal solutions for small-scale instances in accepted computational time. Meanwhile, the proposed matheuristic-oriented iterated greedy algorithm can obtain robust near-optimal solutions for large-scale instances and is superior to other compared algorithms.

关键词： Project scheduling Uncertainty Robust counterpart model mixed integer linear programming Matheuristic

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Optimal Mission Planning for Multiple Agile Satellites Using Modified Dynamic programming

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第3期21卷 279-289页

作者： Lee, Kimoon Kim, Dong-Jin Chung, Dae-Won Lee, Seonho Univ Sci & Technol Daejeon 34113 South Korea Korea Aerosp Res Inst Daejeon 34133 South Korea Korea Aerosp Res Inst Sch Dept Aerosp Syst Engn Gajung Ro South Korea

This paper aims to optimize imaging mission planning for multiple agile Earth observation satellites using a modified dynamic programming (MDP) algorithm. The mission planning problem is mathematically modeled using mixed-integer linear programming, which defines the objective function and constraints. An MDP algorithm is proposed to address the challenge of extended computation time inherent in typical deterministic dynamic programming, aiming to optimize the imaging scheduling problem. Numerical simulations are conducted under various target numbers and satellites in a relatively narrow mission area with a high target density. The simulation results illustrate the stage configuration and target observation sequence over time, and this paper investigates the achievement rate, measured as the ratio of obtained profit and target observations to the total. The trend of computation time is also analyzed across all mission scenarios. Ultimately, the validity of the MDP algorithm is confirmed, as it demonstrates superior performance compared to the genetic algorithm, the branch-and-bound method, and the greedy algorithm.

关键词： Mission Planning Optimization Modified Dynamic programming Agile Earth Observation Satellite mixed integer linear programming

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Interference aware path planning for mobile robots under joint communication and sensing in mmWave networks

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COMPUTER COMMUNICATIONS 2024年 225卷 1-9页

作者： Ren, Yijing Friderikos, Vasilis Kings Coll London Ctr Telecommun Res Dept Engn London WC2R 2LS England

The beyond fifth-generation (B5G) and emerging sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). In this paper, we consider mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells for joint communication and sensing. In such scenarios, the potential close proximity of MRs connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate and network service degradation. To mitigate this effect an interference aware path planning algorithm is proposed by explicitly taking into account both achievable communication and sensing performance. More specifically, the proposed heuristic scheme aims to find paths with minimal interfering time whilst improving the overall joint communication and sensing quality of service. A wide set of numerical investigations reveal that the proposed heuristic path planning scheme for the mmWave networked mobile robots can improve the overall achievable communication throughput and the sensing mutual information by up to 35.6% and 23.6% respectively compared with an interference oblivious scheme. Most importantly, those gains are attained without penalizing noticeably the total travel time of the MRs.

关键词： Beyond 5G Beamforming Industry 4.0 Joint communication and sensing Mobile robots mixed integer linear programming Path planning

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Computational tool for analysis of vehicle-to-home as home backup solution

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ENERGY REPORTS 2024年 11卷 1472-1486页

作者： Daniel, Lea J. King, Carey W. Tuttle, David P. Paxton, William A. Univ Texas Austin Dept Mech Engn Austin TX 78712 USA Univ Texas Austin Energy Inst Austin TX USA Volkswagen Grp Innovat Belmont CA USA

Power outages have been increasing due to major weather events, such as hurricanes, wildfires, and winter storms. Furthermore, when the electric grid is down, rooftop solar photovoltaic (PV) panels cannot operate without battery storage or another way to isolate the home from the bulk grid. Meanwhile, battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) with bidirectional charging can power a home during an outage, a premise called vehicle -to -home (V2H). Electric vehicles typically have much larger batteries than residential behind -the -meter (BTM) battery energy storage systems. This paper explores V2H capabilities during power outages by use of an open -source computational tool developed in Microsoft Excel and Python. The user selects inputs of weather and home size as well as backup source and home load characteristics into the Excel interface. A Python mixed -integer linear programming (MILP) optimization model schedules loads while maximizing PV usage and battery state -of -charge (SoC) and minimizing battery and generator fuel use. The backup duration and electricity consumption of desired load are calculated. The tool was used to execute 504 simulations with differing home size, weather condition, backup source configuration, and load configuration. On average, the backup duration can be increased by about a factor of 9 (from 2.7 to 25 days) by reducing consumption to only critical loads. For certain weather conditions, homes with solar panels can potentially be powered for unlimited duration with enough battery storage.

关键词： Emergency power Electric vehicles Home appliances mixed integer linear programming

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Generic model for capacity allocation on transportation terminals

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 196卷

作者： Berktas, Nihal Zografos, Konstantinos G. Univ Lancaster Ctr Transport & Logist Cent Dept Management Sci Management Sch Lancaster LA1 4YX England Durham Business Sch Dept Mkt & Management Durham DH1 3LB England

Transportation terminals play an important role in the functioning of the transportation system. Therefore, the efficient use of the capacity of transportation terminals is considered a major determinant of the performance of transportation networks. An important decision related to the efficient functioning of congested terminals relates to the optimum allocation of the available capacity to different operators (users). The capacity allocation problem in transportation terminals, such as airports, railroad stations, ports, involves the optimum apportion of the available capacity to different users, such as airlines, rail, and shipping companies, while satisfying operational, and regulatory constraints and requirements. Motivated by the similarities across capacity allocation problems in terminals of different transportation modes and the lack of a unifying framework, this study introduces a generic mixed integer linear programming (MILP) formulation and demonstrates its applicability through a detailed application of the proposed model for rail networks. The generic mathematical model is a generalization of models highly utilized in airport slot allocation. We explicitly present how the model applies to the train timetabling problem and conduct computational experiments using publicly available data. Our computational experiments show that the model consistently achieves optimal solutions across almost all tested cases, including instances where published solutions are suboptimal. The analysis of the results for a specific instance indicates that incorporating station capacity constraints yields the same set of scheduled requests but alters the deviations from the desired arrival and departure times. In contrast, increase in the flexibility of the requested times significantly affect the solution, leading to increase in the number of scheduled trains, deviations, and the overall length of the journey.

关键词： Train timetabling Terminal capacity management mixed integer linear programming

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Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System

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ENERGIES 2024年第18期17卷 4720-4720页

作者： Holtwerth, Alexander Xhonneux, Andre Mueller, Dirk Forschungszentrum Julich GMBH Inst Climate & Energy Syst Energy Syst Engn ICE 1 D-52425 Julich Germany RWTH Aachen Univ Germany Inst Energy Efficient Bldg & Indoor Climate EON Energy Res Ctr D-52056 Aachen Germany

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell.

关键词： model predictive control optimization mixed integer linear programming hybrid battery-hydrogen energy storage system

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A bottom-up multi-objective optimisation approach to dynamic facility layout planning

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第3期62卷 626-643页

作者： Perez-Gosende, Pablo Mula, Josefa Diaz-Madronero, Manuel Univ Politecn Valencia Res Ctr Prod Management & Engn CIGIP Alcoy Spain Univ Politecn Salesiana Ind Engn Dept Guayaquil Ecuador

Dynamic facility layout planning (DFLP) involves determining an appropriate arrangement scheme of the elements making up the production system for each time period into which the planning horizon is divided. When formulating the problem as an optimisation model, using the traditional top-down approach is usual, which firstly determines the block layout (BL) and then the detailed layout (DL) of each work cell. However by this approach, the BL generates area constraints in the detailed phase, which sometimes limit its implementation. In this context, the present paper presents a multi-objective mixed integer non-linear programming (MOMINLP) model that allows the problem to be addressed by considering an alternative approach, known in the literature as the bottom-up approach. The proposed model, called bottom-up mDFLP, considers three objective functions: (1) minimise the total material handling cost (TMHC) and the total rearrangement cost (TRAC);(2) maximise the total closeness rating (TCR) between departments;(3) maximise the area utilisation ratio (AUR). The original MOMINLP is transformed into a more computationally efficient multi-objective mixed integer linear programming (MOMILP) model. The proposed model is applied and validated in a case study of a company in the metal-mechanic sector with 12 departments for three 4-month periods.

关键词： Facility layout facility planning multicriteria decision making non-linear programming mixed integer linear programming

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Research on Distributed Heterogeneous Factory Task Assignment Problem With Loading Efficiency Constraints: A Case Study

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IEEE ACCESS 2024年 12卷 164427-164443页

作者： Zhong, Meisong Zeng, Zhiqiang Hong, Zhiyong Chen, Chuangquan Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China

In a distributed heterogeneous factory (DHF), an order can be assigned to multiple factories for production. Although this method reduces production costs, it can cause reduced loading efficiency and increase transportation costs. Balancing loading efficiency and total cost is a key issue that must be resolved in the task assignment problem (TAP) for DHF. This study investigates the DHFTAP with total cost objective and loading efficiency constraints. To solve this problem, three mixed integer linear programming (MILP) models are constructed based on different production strategies: Model I: split orders based on the combination of factory and warehouse;Model II: merge orders based on product types;Model III: split orders based on tasks. In the experiment, three MILP models and three evolutionary algorithms (EAs) are compared with the rule-based traditional method, and the sensitivity of minimum loading efficiency and the impact of factory load rate are analyzed. The experimental results reveal that MILP and EA can effectively solve the researched problem. Finally, valuable suggestions are provided for enterprises.

关键词： Production facilities Costs Loading Load modeling Manufacturing systems Transportation Heuristic algorithms Metaheuristics Job shop scheduling Biological system modeling Task analysis mixed integer linear programming Evolutionary computation Distributed heterogeneous factory task assignment problem mixed integer linear programming evolutionary algorithm

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Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties

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APPLIED ENERGY 2024年 353卷

作者： Lu, Na Wang, Guangyan Su, Chengguo Ren, Zaimin Peng, Xiaoyue Sui, Quan Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China Zhengzhou Univ Coll Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Power China Huadong Engn Corp Ltd Hangzhou 311122 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and largescale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively.

关键词： Cascade hydro-PV complementary system Uncertainties of inflow runoff and PV power output Interval optimization mixed integer linear programming Extreme scenarios

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